Performance Evaluation of Genetic Algorithm and GA-SA Hybrid Method in Forecasting Dust Storms (Case Study: Khuzestan Province)

Document Type : Research Paper


1 Department of Irrigation and Reclamation Engineering, Faculty of Agriculture Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.

2 Department of Irrigation and Reclamation Engineering, Faculty of Agriculture Engineering & Technology, Campus of Agriculture and Natural Resources, University of Tehran.

3 Postdoctoral Researcher, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy Of Science, Beijing, China.


The increase in dust storms occurrence in recent years in southwestern Iran, especially in Khuzestan province, and consequently the decrease in air quality in these areas, has doubled the importance of forecasting and linking this phenomenon with climate variations. The aim of this study was to investigate the efficiency of hybrid Genetic-Annealing (GA-SA) and Genetic Algorithm (GA) methods for selecting optimal input variables in forecasting the frequency of days with dust storm (FDSD). For this purpose, hourly dust data and meteorological organization codes, as well as climatic data including maximum temperature, minimum temperature, average temperature, total rainfall and maximum wind speed on a seasonal scale with a statistical period of 35 years (1984-2018) in seven synoptic stations in Khuzestan province were used. Then, by having a time series of FDSD index and other climatic variables, it was compared to the efficiency of different modes of input variables, in order to forecasting the frequency of days with dust storm in the next season. The results showed that the hybrid Genetic-Annealing method (GA-SA) had the best performance among all the modes of selecting the input variables; In this case, the evaluation criteria of R, MAE and RMSE varied from 0.91, 0.29, and 0.44 to 0.99, 0.13 and 0.17 in the studied stations, respectively. Also, the average frequency of days with dust storm on a seasonal scale in the studied stations varied from 1.68 to 4.10, respectively, so that with increasing FDSD index in the study station, the predictive accuracy of all modes increased so that in the first forecast state (based solely on the FDSD index), the correlation coefficient between the observational values of the days associated with dust storms and its computational values increased from 0.87 to 0.95. For the second case (forecast based on frequency of days with dust storm and all Auxiliary Characteristics, ie FDSD & AC), the third mode (based on the optimization of the Genetic Algorithm) and the fourth mode (based on the hybrid Genetic-Annealing method) the correlation coefficient also varied from 0.93 to 0.94, 0.91 to 0.97 and 0.94 to 0.99 in the studied stations, respectively. In general, by comparing the modes used, the hybrid Genetic-Annealing method (GA-SA) performed the best, followed by the Genetic Algorithm (GA). The results of this study can be useful in managing the consequences of dust storms and desertification programs in the study areas.


Main Subjects

Abdolshahnejad, M., Khosravi, H., Nazari Samani, A. A., Zehtabian, G. R. & Alambaigi, M. (2020). Determining the Conceptual Framework of Dust Risk Based on Evaluating Resilience (Case Study: Southwest of Iran). Strategic Research Journal of Agricultural Sciences and Natural Resources, 5(1), 33-44. (In Farsi)
Aliyari, M., Teshnehlab, M. & Khaki Sedigh, A. (2008). Short-term forecast of air pollution by neural networks, delayed memory line, gamma and ANFIS with PSO-based educational methods. Control journal, 2(1), 1-19.
Ansari Ghojghar, M. & Araghinejad, Sh. (2018). Investigating the effect of wind speed on the frequency of days with dust storms (Case study: Lorestan province). The fourth national conference on wind erosion and dust storms, Yazd.
Araghinejad, S. (2013). Data-driven modeling: using MATLAB® in water resources and environmental engineering (Vol. 67). Springer Science & Business Media.
Araghinejad, Sh., Ansari Ghojghar, M., Pourgholam-Amiji., Liaghat, A & Bazrafshan, J. (2019). The Effect of Climate Fluctuation on Frequency of Dust Storms in Iran. Desert Ecosystem Engineering Journal, 7(21), 13-32. (In Farsi)
Cao, R., Jiang, W., Yuan, L., Wang, W., Lv, Z., & Chen, Z. (2014). Inter-annual variations in vegetation and their response to climatic factors in the upper catchments of the Yellow River from 2000 to 2010. Journal of Geographical Sciences, 24(6), 963-979.
Cheng, R. (2000). Genetic algorithms and engineering optimization. Wiley-Interscience.
Dahiya, S., Singh, B., Gaur, S., Garg, V. K., & Kushwaha, H. S. (2007). Analysis of groundwater quality using fuzzy synthetic evaluation. Journal of Hazardous Materials, 147(3), 938-946.
Farajzadeh Asl, M. & Alizadeh, Kh. (2011). Spatial Analysis of Dust storm in Iran. The Journal of Spatial Planning, 15(1), 65-84. (In Farsi)
Goudie, A. S., & Middleton, N. J. (2006). Desert dust in the global system. Springer Science & Business Media.
Hahnenberger, M. & Nikoul, K. (2014). Geomorphic and land cover identification of dust sources in the eastern Great Basin of Utah, U.S.A. Journal of Geomorphology, 204(2), 657-672.
Hakimpour, F., Talat Ahary, S. & Ranjbar, A. (2017). The Assessment and Comparison of a Genetic Algorithm, Simulated Annealing and Cuckoo Optimization Algorithm for Optimization of the Facility Location under Competitive Conditions (Case Study: Banks). Journal of Modeling in Engineering, 15(48), 231-246. (In Farsi)
Hassanzadeh, Y., Abdi Kordani, A. & Fakheri Fard, A. (2012). Drought Forecasting Using Genetic Algorithm and Conjoined Model of Neural Network-Wavelet. Journal of Water and Wastewater, 23(3), 48-59. (In Farsi)
Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665–685
Khashei-Siuki, A., Shahidi, A., Pourrezabilondi, M., Amirabadizadeh, M. & Jafarzadeh, A. (2018). Performance Assessment of ANN and SVR for downscaling of daily rainfall in dry regions. Iranian Journal of Soil and Water Research, 49(4), 781-793. (In Farsi)
Kim, D., Chin, M., Kemp, E. M., Tao, Z., Peters-Lidard, C. D., & Ginoux, P. (2017). Development of high-resolution dynamic dust source function-A case study with a strong dust storm in a regional model. Atmospheric environment, 159, 11-25.
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680.
Mehrabi, Sh., Soltani, S. & Jafari, R. (2015). Investigating the Relationship between Climatic Parameters and the Exposure of Greenhouses (Case Study: Khuzestan Province). Journal of Water and Soil Science, 19(71), 69-80. (In Farsi)
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The journal of chemical physics, 21(6), 1087-1092.
Mohammadi, G, H., (2015). Analysis of Atmospheric Mechanisms in Dust Transport over West of Iran. Ph.D. thesis, Tabriz University, 142 pp. (In Farsi)
O’Loingsigh, T., McTainsh, G. H., Tews, E. K., Strong, C. L., Leys, J. F., Shinkfield, P., & Tapper, N. J. (2014). The Dust Storm Index (DSI): a method for monitoring broadscale wind erosion using meteorological records. Aeolian Research, 12, 29-40
Rashki, A., Kaskaoutis, D. G., Goudie, A. S. & Kahn, R. A. (2013). Dryness of ephemeral lakes and consequences for dust activity: the case of the Hamoun drainage basin, southeastern Iran. Science of the Total Environment, 463, 552-564.
Rasoulzadeh Gharibdoosti, S. & Bozorg Haddad, O. (2012). Development and Application of Hybrid (NLP-GA) in the Design and Operation of Pumping Stations. Iranian Journal of Soil and Water Research, 43(2), 129-137. (In Farsi)
Shaker Sureh, F. & Asadi, E. (2019). Meteorological and hydrological drought communication in Salmas Plain. Desert Ecosystem Engineering Journal, 8(22), 89-100. (In Farsi)
Shao, Y., Wyrwoll, K. H., Chappell, A., Huang, J., Lin, Z., McTainsh, G. H. & Yoon, S. (2011). Dust cycle: An emerging core theme in Earth system science. Aeolian Research, 2(4), 181-204.
Sivanandam, S. N. & Deepa. S. N. (2008). Introduction to Genetic Algorithms. Springer-Verlag, Berlin.
Sobhani, B. & Safarian Zengir, V. (2020). Analysis and prediction of Dust phenomenon in the southwest of Iran. Journal of Natural Environmental Hazards, 8(22), 179-178. (In Farsi)
Sobhani, B., Salahi, B. & Goldust, A. (2015). Study the dust and evaluation of its possibility prediction based on statistical methods and ANFIS model in Zabol University.Geography and Development Iranian journal, 13(38), 123-138.  (In Farsi)
Tarokh. M. J. & Naseri, A. (2012). Genetic Algorithm and Hybrid Method to Minimize Total Distribution Cost in Multi-level Supply Chain. Advances in Industrial Engineering, 46(1), 15-26. (In Farsi)
White, S. R. (1984). Concepts of scale in simulated annealing. In AIP Conference Proceedings (Vol. 122, No. 1, pp. 261-270). American Institute of Physics.
Yarmoradi, Z., Nasiri, B., Mohammadi, Gh. H., & Karampour, M. (2018). Trend analysis of dusty day’s frequency in Eastern arts o Iran associated with Climate Fluctuations. Desert Ecosystem Engineering Journal, 7(18), 1-14. (In Farsi)
Zeinali, B. (2016). Investigation of frequency changes trend of days with dust storms in western half of Iran. Journal of Natural Environment hazards, 5(7), 100-87. (In Farsi)